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How to use NNI to do Automatic Feature Engineering?

What is Tabular Data?

Tabular data is an arrangement of data in rows and columns, or possibly in a more complex structure. Usually, we treat columns as features, rows as data. AutoML for tabular data including automatic feature generation, feature selection, and hyper tunning on a wide range of tabular data primitives — such as numbers, categories, multi-categories, timestamps, etc.

Quick Start

In this example, we will show how to do automatic feature engineering on nni.

We treat the automatic feature engineering(auto-fe) as a two steps task. feature generation exploration and feature selection.

We give a simple example.

The tuner call AutoFETuner first will generate a command that to ask Trial the feature_importance of original feature. Trial will return the feature_importance to Tuner in the first iteration. Then AutoFETuner will estimate a feature importance ranking and decide what feature to be generated, according to the definition of search space.

In the following iterations, AutoFETuner updates the estimated feature importance ranking.

If you are interested in contributing to the AutoFETuner algorithm, such as Reinforcement Learning(RL) and genetic algorithm (GA), you are welcomed to propose proposal and pull request. Interface update_candidate_probility() can be used to update feature sample probability and epoch_importance maintains the all iterations feature importance.

Trial receives the configure contains selected feature configure from Tuner, then Trial will generate these feature by fe_util, which is a general SDK to generate features. After evaluating performance by adding these features, Trial will report the final metric to the Tuner.

So when user wants to write a tabular autoML tool running on NNI, she/he should:

1) Have a Trial code to run

Trial's code could be any machine learning code. Here we use main.py as an example:

import nni


if __name__ == '__main__':
    file_name = 'train.tiny.csv'
    target_name = 'Label'
    id_index = 'Id'

    # read original data from csv file
    df = pd.read_csv(file_name)

    # get parameters from tuner
+   RECEIVED_FEATURE_CANDIDATES = nni.get_next_parameter()

+    if 'sample_feature' in RECEIVED_FEATURE_CANDIDATES.keys():
+        sample_col = RECEIVED_FEATURE_CANDIDATES['sample_feature']
+    # return 'feature_importance' to tuner in first iteration
+    else:
+        sample_col = []
+    df = name2feature(df, sample_col)

    feature_imp, val_score = lgb_model_train(df,  _epoch = 1000, target_name = target_name, id_index = id_index)

+    # send final result to Tuner
+    nni.report_final_result({
+        "default":val_score , 
+        "feature_importance":feature_imp
    })

2) Define a search space

Search space could be defined in a JSON file, format as following:

{
    "1-order-op" : [
            col1,
            col2
        ],
    "2-order-op" : [
        [
            col1,
            col2
        ], [
            col3, 
            col4
        ]
    ]
}

We provide count encoding, target encoding, embedding encoding for 1-order-op. We provide cross count encoding, aggerate statistics(min max var mean median nunique), histgram aggerate statistics for 2-order-op. All operations above are classic feature engineer methods, and the detail in here.

Tuner receives this search space and generates the feature by calling generator in fe_util.

For example, we want to search the features which are a frequency encoding (value count) features on columns name {col1, col2}, in the following way:

{
    "COUNT" : [
        col1,
        col2
    ],
}

For example, we can define a cross frequency encoding (value count on cross dims) method on columns {col1, col2} × {col3, col4} in the following way:

{
    "CROSSCOUNT" : [
        [
            col1,
            col2
        ],
        [
            col3,
            col4
        ],
    ]
}

3) Get configure from Tuner

User import nni and use nni.get_next_parameter() to receive configure.

...
RECEIVED_PARAMS = nni.get_next_parameter()
if 'sample_feature' in RECEIVED_PARAMS.keys():
            sample_col = RECEIVED_PARAMS['sample_feature']
else:
    sample_col = []
# raw_feature + sample_feature
df = name2feature(df, sample_col)
...

4) Send final metric and feature importances to tuner

Use nni.report_final_result to send final result to Tuner. Please noted 15 line in the following code.

feature_imp, val_score = lgb_model_train(df,  _epoch = 1000, target_name = target_name, id_index = id_index)
nni.report_final_result({
    "default":val_score , 
    "feature_importance":feature_imp
})

5) Extend the SDK of feature engineer method

If you want to add a feature engineer operation, you should follow the instruction in here.

6) Run expeirment

nnictl create --config config.yml

Test Example

We test some binary-classification benchmarks which come from public resources.

The experiment setting is given in the ./benchmark/benchmark_name/search_sapce.json :

The baseline and the result as following:

Dataset baseline auc automl auc number of cat number of num dataset link
Cretio 0.7516 0.7760 13 26 data link
titanic 0.8700 0.8867 9 1 data link
Heart 0.9178 0.9501 4 9 data link
Cancer 0.7089 0.7846 9 0 data link
Haberman 0.6568 0.6948 2 1 data link